Search Results (390)

Around 20–30% of the horned melon’s weight is peel. This peel is often discarded or underutilized despite containing valuable bioactive compounds. Conventional methods for extracting polyphenols and carotenoids from horned melon peel are typically inefficient, environmentally harmful, or require significant time and energy. The potential of green cloud point extraction (CPE) or green surfactant-based extraction for recovering bioactives with different polarities from this kind of by-product has not been thoroughly investigated. Therefore, this study focused on optimizing CPE process parameters using a one-variable-at-a-time (OVAT) approach. Optimal CPE demonstrated superior yields compared to conventional, ultrasound, microwave, ultrasound-assisted CPE, and microwave-assisted CPE methods. Further, a Plackett–Burman design identified key factors influencing optimal CPE conditions, while artificial neural network (ANN) analysis assessed each input variable’s impact on outcomes. Maximum extraction efficiency for total phenolics (352.49 mg GAE/100 g), total carotenoids (16.59 mg β-carotene/100 g), and antioxidant activity (989.02 μmol TE/100 g) was achieved under conditions of: surfactant type = Tween 80, surfactant concentration = 2%; solid:liquid ratio = 1:100; pH = 6612; equilibration temperature = 35 °C; equilibration time = 60 min; salt type = NaCl; salt concentration = 16.4%; centrifugation speed = 7906× g ; centrifugation time = 13.358 min; and No. of CPE steps = Step 1. This comprehensive approach aimed to enhance the understanding and optimization of CPE for maximizing the recovery of bioactives from the horned melon peel, addressing the inefficiencies of traditional extraction methods. Full article

Reducing CO₂ emissions is becoming a particularly important goal for motorcycle manufacturers. A fully electric transition still seems far away, given the difficulties in creating an electric motorcycle with an acceptable range and mass. This opens up opportunities for the application of hybrid powertrains in motorcycles. Managing mass, cost, and volume is a challenging issue for motorcycles; therefore, an MHEV architecture represents an interesting opportunity, as it is a low-complexity and low-cost solution. Firstly, in this work, an adequate sizing of the powertrain components is studied for the maximum reduction in fuel consumption. This is performed by analyzing many different system configurations with different hybridization ratios. A 1D simulation of the motorcycle traveling along the homologation cycle (WMTC) is performed, and the powerunit use strategy is optimized for each configuration using the Dynamic Programming technique. The results are analyzed in order to highlight the impact of kinetic energy recovery and engine load-point shifting on fuel consumption reduction. The results show the applicability of MHEV technology to road motorcycles, thus providing a useful tool to analyze the cost/benefit ratio of this technology. The developed methodology is also suitable for different vehicles once a specific test cycle is known. Full article

Vegetation plays a key role in trapping sediments and further controlling pollutants. However, few studies were conducted to clarify the erosion and deposition laws of sediments and the influence factors caused by vegetation patch properties, which is not conducive to the revelation of riverbank protection and erosion prevention. Therefore, this study investigated the change in scouring and deposition characteristics around submerged vegetation patches of nine kinds of typical configurations and their influencing factors. Vegetation patches were assembled from three vegetation densities (G/d = 0.83, 1.3, and 1.77, representing dense, medium, and sparse, respectively), and three vegetation patch thicknesses (dn = 170, 400, and 630, representing narrow, usual, and wide, respectively), to measure vegetation patch property influences. Flow velocity, scouring, and deposition characteristics under nine patches were determined by a hydraulic flume experiment, three-dimensional acoustic Doppler velocimetry (ADV), and three-dimensional laser scanner, and then ten geometry and morphology indices were measured and calculated based on the results of laser scanning. Results showed that both vegetation patch density and thickness were positively related to the turbulence kinetic energy (TKE) above the vegetation canopy, and only vegetation patch density was negatively related to the flow velocity above the vegetation canopy. The relation between the product of density and vegetation patch thickness and erosion area in planform (EA) showed a power function (R² = 0.644). Both density and vegetation patch thickness determined the scouring degree, but deposition location and amount did not rely on each one simply. On average, medium density showed the smallest maximum erosion length (MEL), EA, deposition area in planform (DA), and average deposition length (ADL) and a minimum of the above parameters also occurred at narrow vegetation patch thickness. The shape factor of the erosion volume (SFEV), the shape factor of the deposition volume (SFDV), ADL, and MEL of medium density and narrow thickness vegetation patch (G/d = 1.3, dn = 170) were significantly smaller than that of other types of patches. DA and equivalent prismatic erosion depth on the erosion area (EPED) were significantly linearly related (R² = 0.766). Consequently, most sediment was deposited close to the vegetation patch edge. It is suggested that vegetation patch thickness and density should be given to control sediment transport. In particular, natural vegetation growth changes vegetation patch density and then alters vegetation patch thickness. Management and repair need to be first considered. The results of this study shed light on riparian zone recovery and vegetation filter strip mechanism. Full article

This work is an effort to mitigate the existing environmental issues caused by brine discharge from Kuwait’s desalination plants and to find an economical and efficient way of managing reject brine from local desalination plants. Low- and high-resistance membranes (LRMs and HRMs, respectively) were used to produce salt and low-salinity water from brine effluent utilizing an electrodialysis (ED)–evaporator hybrid system. The effect of high current densities of 300, 400, and 500 A/m² and brine flowrates of 450 and 500 L/h on the quality of produced salt and diluate were investigated for LRM and HRM. The recovered salt purity for LRM is up to 90.58%. Results show that the low-resistance membrane (LRM) achieved higher water recovery, energy consumption, desalination rate, operation time and ion removal rate than those of the high-resistance membrane (HRM) under the same operating conditions. The difference in concentration for 300 A/m² between LRM and HRM increased from 0.93% at 10 min to 8.28% at 140 min. The difference in diluate concentration effluent is negligible for both membranes, whereas LRM produced higher concentrate effluent than HRM for all current densities and low flowrate (400 L/h). The maximum difference between LRM and HRM (with LRM achieving higher concentrations) is 10.7% for 400 A/m². The permselectivity of LRM for monovalent cations decreased with current density, whereas the effect on permselectivity for HRM was insignificant for the current density values. The addition of a neutral cell was effective in reducing the buildup of divalent ions on the inner membrane of the cathode side. Full article

Short-lived climate pollutants (SLCPs) and municipal solid wastes (MSWs) have been found to be viable sources of clean energy. This study integrates the Intergovernmental Panel on Climate Change (IPCC) guidelines for methane flow rate estimation in the software Long-Range Energy Alternatives Planning-Integrated Benefit Calculator (LEAP-IBC) system to estimate and project the methane emissions coming from the waste generated by Metro Manila, disposed in sanitary landfills. It aims to analyze the environmental impacts of the emissions coming from the non-energy sector using the IBC feature of LEAP and by developing two scenarios with 2010 and 2050 as the base and end years: the baseline and methane recovery scenario, where the latter represents the solid waste management undertaken to counter the emissions. Under the baseline, 97.30 million metric tonnes of methane emissions are expected to be produced and are predicted to continuously increase. In the same scenario, the cities of Quezon, Manila, and Caloocan account for the biggest methane emissions. On the other hand, in the methane recovery scenario, the methane emissions are expected to have a decline of 36% from 127.036 to 81.303 million metric tonnes by 2025, 52% from 135.358 to 64.972 million metric tonnes by 2030, and 54% from 150.554 to 69.254 million metric tonnes by 2040. For the 40-year projection of the study under the 100-year global warming potential analysis, a total of 10,249 million metric tonnes of CO₂ equivalent is avoided in the methane recovery compared to the BAU, and a maximum of 0.019 °C temperature increase can also be avoided. Moreover, electricity costs without LFG technology increase from 2.21 trillion to 8.75 trillion, while costs with LFG technology also rise but remain consistently lower, ranging from 2.20 trillion to 8.74 trillion. This consistent reduction in electricity costs underscores the long-term value and importance of adopting LFG technology, even as its relative savings impact diminishes over time. Finally, the fixed effects and random effects panel data regression analysis reinforces and asserts that the solid waste management is really improved by means of the methane recovery technology, leading the methane emissions to decrease. Full article

The wastewater produced by the pharmaceutical industry is highly organic and toxic. Dual-chambered microbial fuel cells (DMFCs) may represent a sustainable solution to process wastewater while simultaneously recovering its energy content. DMFCs are bio-electrochemical devices that employ microorganisms to transform the chemical energy of organic compounds into electrical energy. This study aims to demonstrate the feasibility of a DMFC with a manganese cobalt oxide-coated activated carbon fiber felt (MnCo₂O₄-ACFF) electrode to treat pharmaceutical industry wastewater (PW) and exploit its energy content. The proposed technology is experimentally investigated considering the effect of the organic load (OL) on the system performance in terms of organic content removal and electricity production. As per the experimental campaign results, the optimum OL for achieving maximum removal efficiencies for total chemical oxygen demand, soluble oxygen demand, and total suspended solids was found to be 2 g COD/L. At this value of OL, the highest current and power densities of 420 mA/m² and 348 mW/m² were obtained. Therefore, based on the outcomes of the experimental campaign, the (MnCo₂O₄-ACFF) electrode DMFC technique was found to be a sustainable and effective process for the treatment and energy recovery from PW. Full article

Worldwide, the growing demand for energy has been largely met through power cycles utilizing fossil fuels. Combined cycles, which integrate a gas turbine with a steam cycle, prove to be the best alternative due to their power generation capacity and high efficiencies. This efficiency is primarily attributed to the ability to harness exhaust gases to generate steam in the heat recovery boiler, allowing additional power generation through the steam turbine. Currently, there is a quest for the integration of low-temperature power cycles to maximize the utilization of residual thermal energy flows for power generation. Therefore, this work conducts an exergetic optimization of a power trigeneration system aimed at maximizing exergetic efficiency. This system includes a gas turbine and a steam cycle coupled with three different configurations of the Organic Rankine Cycle (ORC): a simple ORC, a supercritical ORC, and an ultracritical ORC. The ORC configurations are analyzed using eight organic working fluids, namely R1234yf, R290, R134a, R1234ze, R152a, R600a, R245fa, and R123. The results show that the maximum exergetic efficiency is achieved by using R152a in the ultracritical ORC configuration coupled with the combined cycle, achieving an exergetic efficiency of 55.79%. Furthermore, the maximum power generated is attained by the steam cycle with 85,600.63 kW and 3101.21 kW for the ultracritical ORC. Full article

Microbial fuel cells (MFCs) are sustainable energy recovery systems because they use organic waste as biofuel. Using critical raw materials (CRMs), like platinum-group metals, at the cathode side threatens MFC technology’s sustainability and raises costs. By developing an efficient electrode design for MFC performance enhancement, CRM-based cathodic catalysts should be replaced with CRM-free materials. This work proposes developing and optimizing iron-based air cathodes for enhancing oxygen reduction in MFCs. By subjecting iron phthalocyanine and carbon black pearls to controlled thermal treatments, we obtained Fe-based electrocatalysts combining high surface area (628 m² g⁻¹) and catalytic activity for O₂ reduction at near-neutral pH. The electrocatalysts were integrated on carbon cloth and carbon paper to obtain gas diffusion electrodes whose architecture was optimized to maximize MFC performance. Excellent cell performance was achieved with the carbon-paper-based cathode modified with the Fe-based electrocatalysts (maximum power density-PD_max = 1028 mWm⁻²) compared to a traditional electrode design based on carbon cloth (619 mWm⁻²), indicating the optimized cathodes as promising electrodes for energy recovery in an MFC application. Full article

Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction tasks. This study offers a comprehensive review of predominant AI-based approaches to photovoltaic (PV) energy forecasting, with a particular emphasis on artificial neural networks (ANNs). We introduce a revolutionary methodology that amalgamates the predictive capabilities of ANN with the precision control afforded by the minimum-risk problem and sliding mode control (MRP-SMC), thereby revolutionizing the PV panel performance enhancement. Building upon this methodology, our hybrid approach utilizes the ANN as a proficient weather forecaster, accurately predicting the temperature and solar radiation levels impacting the panels. These forecasts serve as guiding principles for the MRP-SMC algorithm, enabling the proactive determination of the Maximum Power Point (MPP). Unlike conventional methods that grapple with weather unpredictability, the MRP-SMC algorithm transforms stochastic optimization challenges into controllable deterministic risk problems. Our method regulates the boost converter’s work cycle dynamically. This dynamic adaptation, guided by environmental predictions from ANNs, unlocks the full potential of PV panels, maximizing energy recovery efficiency. To train the model, we utilized a large dataset comprising 60,538 temperature and solar radiation readings from the Department of Systems Engineering and Automation at the Faculty of Engineering in Vitoria (University of the Basque Country). Our approach demonstrates a high regression coefficient (R = 0.99) and low mean square error (MSE = 0.0044), underscoring its exceptional ability to predict real energy values. In essence, this study proposes a potent fusion of artificial intelligence and control mechanisms that unleash the untapped potential of photovoltaic panels. By utilizing forecasts to guide the converter, we are paving the way for a future where solar energy shines brighter than ever. Full article

Zinc oxide (ZnO) is considered to be one of the most explored and reliable sensing materials for UV detection due to its excellent properties, like a wide band gap and high exciton energy. Our current study on a photodetector based on tetrapodal ZnO (t-ZnO) reported an extremely high UV response of ~9200 for 394 nm UV illumination at 25 °C. The t-ZnO network structure and morphology were investigated using XRD and SEM. The sensor showed a UV/visible ratio of ~12 at 25 °C for 394 nm UV illumination and 443 nm visible illumination. By increasing the temperature, monotonic decreases in response and recovery time were observed. By increasing the bias voltage, the response time was found to decrease while the recovery time was increased. The maximum responsivity shifted to higher wavelengths from 394 nm to 400 nm by increasing the operating temperature from 25 °C to 100 °C. The t-ZnO networks exhibited gas-sensing performances at temperatures above 250 °C, and a maximum response of ~1.35 was recorded at 350 °C with a good repeatability and fast recovery in 16 s for 100 ppm of n-butanol vapor. This study demonstrated that t-ZnO networks are good biosensors that can be used for diverse biomedical applications like the sensing of VOCs (volatile organic compounds) and ultraviolet detection under a wide range of temperatures, and may find new possibilities in biosensing applications. Full article

As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, making it difficult to achieve stable and precise leader-following. This paper decouples the leader-following control into speed and curvature control to address such issues. It utilizes model reference adaptive control to establish reference models for the speed and curvature subsystems and designs corresponding parameter adaptive control laws. This control method enables the actual vehicle speed and curvature to effectively track the response of the reference model, thereby addressing the impact of frequent changes in the steering resistance coefficient. Furthermore, this paper demonstrates significant improvements in leader-following performance through a series of simulations and experiments. Compared with the traditional PID control method, the results shows that the maximum following distance has been reduced by at least approximately 12% (ensuring the ability to keep up with the leader), the braking distance has effectively decreased by 22% (ensuring a safe distance in an emergency braking scenario and improving energy recovery), the curvature tracking accuracy has improved by at least 11% (improving steering performance), and the speed tracking accuracy has increased by at least 3.5% (improving following performance). Full article

In order to reduce the negative influence of wind speed randomness and prediction error on frequency modulation, the reliability of the wind storage system was assessed effectively. In the wind storage frequency modulation system, a state of charge (SOC) adaptive adjustment method for wind speed randomness is proposed. Firstly, through the correlation analysis between the standby capacity of frequency modulation and the output power of wind turbine, the uncertainty of its frequency modulation capacity is revealed. Secondly, in view of the uncertainty of wind turbine frequency modulation, the output power of energy storage frequency modulation is optimized with the goal of minimizing the frequency modulation power deviation of the wind storage front under the framework of model predictive control, and the improved whale optimization algorithm (WOA) is used to solve the problem. Finally, the simulation results show that, under the given 5 min continuous disturbance, the root mean square of frequency regulation of the proposed restoration method is reduced by 56.65% compared to the SOC recovery base point set to 0.5. Under continuous large perturbations, the maximum frequency deviation is reduced by 0.0455 Hz. This effectively shows that this method can not only improve the frequency modulation reliability of wind power system but also improve the continuous frequency modulation capability of energy storage system. Full article

In this study, the plasma graft polymerization technique was used to graft glycidyl methacrylate (GMA) onto polypropylene (PP) melt-blown fibers, which were subsequently aminated with N-methyl-D-glucamine (NMDG) by a ring-opening reaction, resulting in the formation of a boron adsorbent denoted as PP-g-GMA-NMDG. The optimal conditions for GMA concentration, grafting time, grafting temperature, and the quantity of NMDG were determined using both single factor testing and orthogonal testing. These experiments determined the optimal process conditions to achieve a high boron adsorption capacity of PP-g-GMA-NMDG. Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), energy dispersion spectrum analysis (EDS), and water contact angle measurements were performed to characterize the prepared adsorbent. Boron adsorption experiments were carried out to investigate the effects of pH, time, temperature, and boron concentration on the boron adsorption capacity of PP-g-GMA-NMDG. The adsorption isotherms and kinetics of PP-g-GMA-NMDG for boron were also studied. The results demonstrated that the adsorption process followed a pseudo-second-order kinetic model and a Langmuir isothermal model. At a pH of 6, the maximum saturation adsorption capacity of PP-g-GMA-NMDG for boron was 18.03 ± 1 mg/g. In addition, PP-g-GMA-NMDG also showed excellent selectivity for the adsorption of boron in the presence of other cations, such as Na⁺, Mg²⁺, and Ca²⁺, PP-g-GMA-NMDG, and exhibited excellent selectivity towards boron adsorption. These results indicated that the technique of preparing PP-g-GMA-NMDG is both viable and environmentally benign. The PP-g-GMA-NMDG that was made has better qualities than other similar adsorbents. It has a high adsorption capacity, great selectivity, reliable repeatability, and easy recovery. These advantages indicated that the adsorbents have significant potential for widespread application in the separation of boron in water. Full article

Static waste heat recovery, by means of thermoelectric generator (TEG) modules, constitutes a fast-growing energy harvesting technology on the way towards greener transportation. Many commercial solutions are already available for small internal combustion engine (ICE) vehicles, whereas further development and cost reductions of TEG devices expand their applicability at higher-power transportation means (i.e., ships and aircrafts). In this light, the integration of waste heat recovery based on TEG modules in a shipboard distribution network is studied in this work. Several voltage step-up techniques are considered, whereas the most suitable ones are assessed via the LTspice simulation platform. The design procedure of the selected LLC resonant converter is presented and analyzed in detail. Furthermore, a flexible control strategy is proposed, capable of either output voltage regulation (constant voltage) or maximum power point tracking (MPPT), according to the application demands. Finally, both simulations and experiments (on a suitable laboratory testbench) are performed. The obtained measurements indicate the high efficiency that can be achieved with the LLC converter for a wide operating area as well as the functionality and adequate performance of the control scheme in both operating conditions. Full article

In order to reduce greenhouse gas emissions and achieve global decarbonisation, it is essential to find sustainable and renewable alternatives for electricity production. In this context, the development of distributed generation systems, with the use of thermodynamic and photovoltaic solar energy, wind energy and smart grids, is fundamental. ORC power plants are the most appropriate systems for low-grade thermal energy recovery and power conversion, combining solar energy with electricity production. The application of a micro-scale ORC plant, coupled with Parabolic Trough Collectors as a thermal source, can satisfy domestic user demand in terms of electrical and thermal power. In order to develop a micro-scale ORC plant, a commercial hermetic scroll compressor was tested as an expander with HFC-245fa working fluid. The tests required the construction of an experimental bench with monitoring and control sensors. The aim of this study is the description of the scroll performances to evaluate the application and develop optimization strategies. The maximum isentropic effectiveness is reached for an expansion ratio close to the volumetric expansion ratio of the scroll, and machine isentropic effectiveness presents small variations in a wide range of working conditions. The filling factor is always higher than one, due to leakage in the mechanical seals of the scroll or other inefficiencies. This study demonstrates that using a commercial scroll compressor as an expander within an ORC system represents a valid option for such applications, but it is necessary to improve the mechanical seals of the machine and utilize a dedicated control strategy to obtain the maximum isentropic effectiveness. Full article